Dataset Construction for Radiative Transfer Modeling: Accounting for Spherical Curvature Effect on the Simulation of Radiative Transfer Under Diverse Atmospheric Scenarios

Conventional radiative transfer (RT) models often adopt the plane-parallel (PP) approximation, which neglects Earth’s curvature and leads to significant optical path errors under large solar or sensor zenith angles, particularly for high-latitude regions and twilight conditions. The spherical Monte Carlo method offers high accuracy but is computationally expensive, and the commonly used pseudo-spherical (PSS) approximation fails when the viewing zenith angle exceeds 80°. With the increasing application of machine learning in atmospheric science, the efficiency and angular limitations of spherical RT simulations may be overcome. This study provides a physical and quantitative foundation for developing a hybrid RT framework that integrates physical modeling with machine learning. By systematically quantifying the discrepancies between PP and spherical RT models under diverse atmospheric scenarios, key influencing factors—including wavelength, solar and viewing zenith angles, aerosol properties (e.g., single scattering albedo and asymmetry factor), and PP-derived radiance—were identified. These variables significantly affect spherical radiative transfer and serve as effective input features for data-driven models. Using the corresponding spherical radiance as the target variable, the proposed framework enables rapid and accurate inference of spherical radiative outputs based on computationally efficient PP simulations.